Prediction of new scientific collaborations through multiplex networks
Visualitza/Obre
10.1140/epjds/s13688-021-00282-x
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/366897
Tipus de documentArticle
Data publicació2021-12
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement 3.0 Espanya
Abstract
The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks.
CitacióTuninetti, M. [et al.]. Prediction of new scientific collaborations through multiplex networks. "EPJ Data Science", Desembre 2021, vol. 10, núm. 1, p. 25:1-25:10.
ISSN2193-1127
Altres identificadorshttps://zaguan.unizar.es/record/110651?ln=es
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s13688-021-00282-x.pdf | 1,433Mb | Visualitza/Obre |